Is your phylogeny informative?

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Is your phylogeny informative?

  1. 1. Is your phylogeny informative? Measuring the power of comparative methods Carl Boettiger University of California, Davis SICB 2011Fell free to share live
  2. 2. Two QuestionsRight Model? Sufficient Data? Currently we worry that we don’t have the right models. We should worry that we don’t have the power to know if we have the right models.
  3. 3. Right Model? h enoug m ed ? cram e tree we to th ave gy inH lo bio (diagram shows tree under weight of so many models)
  4. 4. Sufficient Data?Is the data-set big enough? ... informative about the things we want to estimate?
  5. 5. A model of “phylogenetic signal” Are closely related species really more similar? al” on g sig n str al” “ n No sig 0 1“ an wni (Bro ion) mot
  6. 6. A simulation-based test of this method
  7. 7. A simulation-based test of this method al” on g s i gn str al” “ n “No sig 0 1
  8. 8. A simulation-based test of this method e ue valu Tr estimates al” on g sig n str al” “ n “No sig 0 1 w nian (Bro ion) mot
  9. 9. What went wrong?Was supposed to address theproblem of adequate data... Instead, its just fallen prey to the problem Insufficient signal to estimate signal
  10. 10. “Has the pendulum swung too far?”
  11. 11. ree e e r t at Bi gg s ti m e an da c b lam ter be t ve r s s o ci e ha pe 0s ) ← 30 23 (vs al” on g sig n str al” “ n“No sig 0 1
  12. 12. Not just size that matters.... e is quit f a data ate o l2 3 tax ut estim Origina tive abo e, sigma a t inform ication ra sif diver
  13. 13. Is the phylogeny informative? Depends on the question Depends on the amount of data x ple ta to com da m ore the dd have we a we as , do ?So dels hem mo tify t jus uld wo ? ow now H k we
  14. 14. Process vs Pattern Models represent mechanisms More complex models fit the data betterSo can we tell when weve matchedthe mechanism and when weveonly matched the pattern? Yes!
  15. 15. Phylogenetic Monte Carlo rom la te f el → i mu e mo dS pl sim
  16. 16. Phylogenetic Monte Carlo rom la te f el → i mu e mo dS pl sim odel s b oth m ← Fits data to this
  17. 17. Phylogenetic Monte Carlo rom la te f el → i mu e mo dS pl sim odel s b oth m ← Fits data to this LogLik(complex) LogLik(simple)
  18. 18. Phylogenetic Monte Carlo rom la te f el → i mu e mo dS pl sim odel s b oth m ← Fits data to this LogLik(complex) LogLik(simple) frequency Likelihood ratio
  19. 19. Phylogenetic Monte Carlo rom la te f el → i mu e mo dS pl sim odel s b oth m ← Fits data to this LogLik(complex) LogLik(simple) frequency Likelihood ratio
  20. 20. Phylogenetic Monte Carlo rom la te f el → i mu e mo dS pl sim odel s b oth m ← Fits data to this LogLik(complex) LogLik(simple) frequency Likelihood ratio
  21. 21. Phylogenetic Monte Carlo x ple comdel → rom mo la te f el → i mu e mo dS pl sim odel s b oth m ← Fits data to this LogLik(complex) LogLik(simple) frequency Likelihood ratio
  22. 22. Phylogenetic Monte Carlo x ple comdel → rom mo la te f el → i mu e mo dS pl sim odel s b oth m ← Fits data to this LogLik(complex) LogLik(simple) frequency Likelihood ratio
  23. 23. A reliable way to justify the complex model io ← Rat d for observe ata d A nolesProb of false positive: 0.0115Power at 95% confidence: 0.99
  24. 24. Greater overlap indicates less information,less power to distinguish
  25. 25. And we can identify cases where the treereally has no information at all
  26. 26. Preferring the simpler model isnt always a lack of power Avoid over- fittingNo trouble withnon-nestedmodels
  27. 27. Compared to what we already do? a s no A IC h o f n n otio r e pow oses del cho mo AIC wrong d o the pite go des r! e pow About as good as flipping a coin...
  28. 28. Larger trees can identify more subtle differences between models
  29. 29. Conclusions Weve been worried about the right model. First we must worry if we have enough data to tell. Without this, we will often choose wrong models. Future lies in big trees! PMC: an R package for Phylogenetic Monte Carlohttps://github.com/cboettig/Comparative-Phylogenetics/
  30. 30. Acknowledgements● Graham Coop● Peter Ralph● Wainwright Lab

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